The association between C-reactive protein-triglyceride glucose index and the risk of new-onset stroke in middle-aged and elderly individuals: A Retrospective Cohort Study

The association between C-reactive protein-triglyceride glucose index and the risk of new-onset stroke in middle-aged and elderly individuals: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association between C-reactive protein-triglyceride glucose index and the risk of new-onset stroke in middle-aged and elderly individuals: A Retrospective Cohort Study Mingchen Wang, Guan Wang, Xukun Bi, Huile Wang, Xueyan Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8100524/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective The impact of the triglyceride-glucose (TyG) index on stroke incidence has been investigated in numerous studies. However, the relationship between the C-reactive protein-triglyceride-glucose (CTI) index, which is a new marker of insulin resistance and inflammation, and stroke is unclear. Methods We analysed data from 7,295 participants in the China Health and Retirement Longitudinal Study (CHARLS). The association of the CTI index with incident stroke was assessed using something called 'logistic regression'. Potential nonlinearity was explored using restricted cubic splines (RCS), and the stability of the results was evaluated through subgroup analyses. Furthermore, we compared the predictive performance of the CTI and TyG indices by calculating their respective areas under the receiver operating characteristic (ROC) curve. Results During the follow-up period, 456 participants experienced a stroke. After adjusting for confounding factors (Model 3), higher and progressively increasing CTI indices in the Q2 (OR = 1.48, 95% CI 1.08–2.05) and Q3 (OR = 1.75, 95% CI 1.27–2.43) groups were associated with an increased risk of new-onset stroke. Although the OR for Q4 was elevated, it was not statistically significant (OR = 1.32, 95% CI 0.92–1.92). RCS analysis revealed a U-shaped relationship between CTI index and stroke risk. ROC analysis revealed that the CTI index had an area under the curve (AUC) of 0.607 (95% confidence interval: 0.403–0.726), whereas the TyG index had an AUC of 0.553 (95% confidence interval: 0.713–0.373). This finding suggests that the CTI index is a more reliable indicator of stroke risk than the TyG index. Conclusions Overall, this study demonstrates that the CTI index is an independent risk factor for new-onset stroke. Our findings suggest that the most cost-effective approach to stroke prevention is to implement interventions targeting individuals whose CTI index begins to rise from moderate levels. C-reactive protein-triglyceride glucose index stroke China Health and Retirement Longitudinal Study inflammation retrospective study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke is the primary cause of death and disability, and its global incidence is increasing, which heightens the risk of adverse outcomes 1 . In China, stroke accounts for the highest disability-adjusted life-years lost of all diseases. Driven by population ageing and the widespread prevalence of risk factors such as hypertension and inadequate control measures, the future burden of this disease is projected to continuously escalate, imposing significant economic and social pressures on families and wider society 2 . Despite advances in prevention and treatment, the incidence of stroke continues to rise 3 , prior research indicates that insulin resistance (IR) is a factor that increases the risk of stroke 4 , 5 . Consequently, identifying stroke risk factors and implementing effective interventions are crucial for prevention and delaying disease progression. IR denotes diminished physiological insulin action within the body and may contribute to the development of atherosclerosis and the onset and progression of stroke 6 , 7 . Due to its simplicity and practicality, the TyG index is widely used by researchers as a marker for IR 8 . There is substantial evidence supporting its efficacy in predicting conditions such as hypertension, atrial fibrillation, diabetes mellitus and stroke 9 , 10 . Furthermore, inflammation is recognised as a critically important risk factor for stroke 11 . It significantly increases stroke incidence by promoting atherosclerosis, impairing vascular endothelial function and increasing thrombogenicity 12 . C-reactive protein (CRP), a non-specific inflammatory marker, is closely associated with stroke risk and has emerged as a promising biomarker for stroke risk assessment 13 , 14 . Insulin resistance and vascular inflammation are key drivers in the pathogenesis of stroke 15 , 16 . Therefore, developing composite indicators reflecting insulin resistance and inflammation as tools for predicting stroke is of considerable importance. Ruan et al. first proposed the CTI 17 , which comprehensively reflects inflammation and insulin resistance, demonstrating strong predictive value for prognosis in diabetic patients, cancer patients, and cardiovascular disease incidence 18 – 20 . However, the association between CTI and stroke risk remains unclear. In order to address these critical research gaps, we analysed data from the China Health and Retirement Longitudinal Study (CHARLS) in order to explore the complex relationship between CTI and stroke risk. This provides further evidence for the practical application of CTI in real-world settings. Methods Study design and population Our research data analysis is based on the CHARLS, which is a nationwide study designed to examine issues related to ageing. The first survey was conducted in 2011–12 (Wave 1), with subsequent follow-up surveys taking place every two to three years. The survey sample covers 150 county-level and 450 village-level units, comprising participants aged 45 years and over. All participants provided written informed consent. The analysis included 17,705 individuals who completed blood tests at baseline (Wave 1). After excluding those who met the following criteria, the final cohort comprised 7,295 individuals: age < 45 years; missing baseline data for age, sex, place of origin, marital status, triglycerides (TG), fasting blood glucose (FBG) or body mass index(BMI) ≤ 100; a history of stroke prior to 2011; the use of hypoglycaemic or lipid-lowering medication; or missing baseline covariate data. Figure 1 illustrates the detailed screening process. Assessment of the CTI index Data on age, gender, TG, FBG and CRP were collected from the first round of surveys. The following index was calculated using an established method 21 : CTI = 0.412 × Ln (CRP [mg/L]) + Ln (TG [mg/dl] × FPG [mg/dl])/2. Assessment of new-onset stroke The diagnosis of new-onset stroke was based on self-reported data. Stroke events that occurred during the fourth follow-up wave (in 2018) were considered outcome measures. Respondents who answered 'yes' to the interviewer's question 'Have you ever been diagnosed with a stroke by a doctor?' were classified as stroke patients. Participants who had experienced a stroke in 2011 were excluded. Patients who were subsequently diagnosed with a stroke during the follow-up period up to 2018 were included in the study according to our definition of new-onset stroke. Assessment of covariates The following covariates have been selected: (1) Socio-demographic characteristics: age; gender (male/female); marital status (married/unmarried); place of residence (urban/rural); educational attainment (university and above/secondary school/primary school); (2) Lifestyle and dietary habits: Smoking (“Yes” or “No”); alcohol consumption (“Drinks less than once a month”, “Drinks more than once a month” or “Does not drink”); average nightly sleep duration over the past month; (3) Medical history: Hypertension (present/absent); diabetes (present/absent); (4) Laboratory tests, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and blood glucose; (5) Physical examination, including BMI. Statistical analysis The statistical analysis was based on data derived from CHARLS surveys conducted between 2011 and 2018. Our study encompassed 7,295 participants. For continuous variables, the data are presented as the mean (standard deviation, SD) or the median (interquartile range, IQR), and for categorical variables, the data are expressed as percentages. We used multivariate logistic regression models to examine the relationship between the CTI index and new-onset stroke, adjusting for relevant variables within the models. Model 1 was unadjusted, Model 2 included adjustments for gender, age, place of residence, marital status and educational attainment, and Model 3 incorporated further adjustments for smoking and drinking status, hypertension, sleep duration, diabetes, blood glucose levels, BMI, HDL and LDL.To explore potential non-linear relationships between the CTI index and stroke risk, we subsequently applied RCS analysis. Subgroup analyses were then employed to evaluate multiple regression stratified by subgroup, examining interactions to determine whether different covariates, such as sociodemographic, lifestyle and health-related factors, influenced the association between the CTI index and stroke risk. Finally, we employed ROC curve analysis to compare the predictive capabilities of the CTI and TyG indices for stroke. Statistical analysis was conducted using R version 4.4.3, with two-tailed P-values below 0.05 being considered statistically significant. Results Baseline characteristics of participants The participant screening process is illustrated in Fig. 1 . Table 1 shows that the study included 7,295 participants, with an average age of 58.52 ± 8.75 years. Of these participants, 4,006 (55%) were female. By the end of the follow-up period, 456 participants had experienced a stroke. The highest incidence rates were observed in quartiles 1, 2, 3 and 4, at 3.7%, 5.9%, 8.0% and 7.4% respectively. Furthermore, individuals in the highest quartile of CTI were more likely to be female, reside in rural areas, have shorter sleeping times, smoke less frequently, and abstain from alcohol consumption than those in the lowest quartile. They also exhibited a higher prevalence of hypertension, diabetes mellitus, and dyslipidaemia. Regarding physical examinations and laboratory assessments, higher CTI quartiles were associated with higher BMI and blood glucose levels. Table 1 Patient demographics and baseline characteristics Characteristic Overall N = 7295 Q1 N = 1824 Q2 N = 1824 Q3 N = 1823 Q4 N = 1824 p -value Age(years, M ± SD) 58.52 ± 8.75 58.47 ± 9.16 58.58 ± 8.77 58.95 ± 8.68 58.06 ± 8.36 0.033 Gender, n(%) < 0.0001 Female 4 006 (55%) 858 (47%) 985 (54%) 1 077 (59%) 1 086 (60%) Male 3 289 (45%) 966 (53%) 839 (46%) 746 (41%) 738 (40%) Education, n(%) 0.97 College+ 92 (1.3%) 22 (1.2%) 27 (1.5%) 20 (1.1%) 23 (1.3%) High shcool 638 (8.7%) 162 (8.9%) 158 (8.7%) 161 (8.8%) 157 (8.6%) Primary- 6 565 (90%) 1 640 (90%) 1 639 (90%) 1 642 (90%) 1 644 (90%) Marital, n(%) 0.013 Married 6 229 (85%) 1 534 (84%) 1 552 (85%) 1 544 (85%) 1 599 (88%) No 1 066 (15%) 290 (16%) 272 (15%) 279 (15%) 225 (12%) Location, n(%) 0.0003 City 485 (6.6%) 99 (5.4%) 115 (6.3%) 111 (6.1%) 160 (8.8%) Village 6 810 (93%) 1 725 (95%) 1 709 (94%) 1 712 (94%) 1 664 (91%) Smoking, n(%) 2 759 (38%) 781 (43%) 707 (39%) 642 (35%) 629 (34%) < 0.0001 Drinking, n(%) < 0.0001 Drink but less than once a month 572 (7.8%) 156 (8.6%) 146 (8.0%) 130 (7.1%) 140 (7.7%) Drink more than once a month 1 821 (25%) 527 (29%) 459 (25%) 410 (22%) 425 (23%) No 4 902 (67%) 1 141 (63%) 1 219 (67%) 1 283 (70%) 1 259 (69%) Sleeping Time, n(%) 6.37± (1.87) 6.37± (1.86) 6.31± (1.87) 6.34± (1.88) 6.45± (1.85) 0.13 Hypertension, n(%) 1 665 (23%) 276 (15%) 339 (19%) 469 (26%) 581 (32%) < 0.0001 Diabetes, n(%) 350 (4.8%) 32 (1.8%) 56 (3.1%) 78 (4.3%) 184 (10%) < 0.0001 BMI(Kg/m², M ± SD) 23.60± (3.95) 22.21± (3.39) 23.10± (4.00) 23.93± (4.02) 25.16± (3.76) < 0.0001 Glucose (mg/dl, M ± SD) 109.14± (33.87) 95.34± (14.11) 101.15± (13.63) 106.68± (18.71) 133.37± (54.83) < 0.0001 HDL-C (mg/dl, M ± SD) 51.40± (15.35) 60.67± (15.08) 54.82± (14.04) 49.16± (12.85) 40.96± (11.89) < 0.0001 LDL-C (mg/dl, M ± SD) 116.67± (34.67) 109.11± (28.87) 119.45± (30.93) 123.64± (34.38) 114.48± (41.49) < 0.0001 CTI 8·73± (0·84) 7·75± (0·30) 8·39± (0·15) 8·91± (0·16) 9·85± (0·57) < 0·0001 TyG (mg/dl, M ± SD) 8.68± (0.67) 7.93± (0.25) 8.41± (0.11) 8.80± (0.12) 9.56± (0.51) < 0.0001 Stroke, n(%) 456 (6.3%) 67 (3.7%) 108 (5.9%) 146 (8.0%) 135 (7.4%) < 0.0001 Note:HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; Sleeping Time:Average Hours for One Night Sleeping Time During the Past Month; BMI: body mass index; TyG: triglyceride-glucose index; Correlation between CTI Index and Risk of New Stroke To investigate the association between CTI indices and stroke risk, this study employed logistic regression analysis so that the relationship between CTI quartiles and stroke risk could be assessed, and this was quantified through the calculation of odds ratios (OR) and 95% confidence intervals (95% CI). Table 2 presents the results of the multivariate regression analysis. The results indicate that, in the fully adjusted model (Model 3), each additional unit of CTI in the Q2 group was associated with a 48% increased risk of stroke compared with the Q1 group (OR = 1.48, 95% CI: 1.08–2.05). For the Q3 group, each additional unit of CTI was associated with a 75% increased risk of stroke compared with the Q1 group (OR = 1.75, 95% CI: 1.27–2.43). This suggests that stroke risk increases with higher CTI levels. However, this association was not statistically significant for the Q4 group (the highest quartile) (OR = 1.32, 95% CI: 0.92–1.92). Table 2 Prospective associations between baseline CTI with follow-up incident stroke Characteristic Model 1 p- value Model 2 p- value Model 3 p- value Q1 ref ref ref Q2 1.65(1.21, 2.26) 0.0017 1.68(1.23, 2.31) 0.0012 1.48(1.08, 2.05) 0.016 Q3 2.28(1.70, 3.09) < 0.0001 2.31(1.72, 3.13) < 0.0001 1.75(1.27, 2.43) 0.0006 Q4 2.10(1.56, 2.85) < 0.0001 2.21(1.64, 3.01) < 0.0001 1.32(0.92, 1.92) 0.14 P for trend 0.016 0.018 0.005 Note: Model 1 was crude model. Model 2 was adjusted for age, gender, education level, location and marital status. Model 3 was further for smoking, drinking, sleep, hypertension, diabetes, ldl Cholesterol, hdl Cholesterol, BMI and glucose. Dose-response relationship between CTI index and stroke risk RCS analysis was employed to evaluate the potential non-linear association between the CTI index and stroke risk. Figure 2 illustrates these findings, revealing a U-shaped relationship between the CTI index and stroke within the fully adjusted model ( P _overall = 0.0004, P _non-linear = 0.0011). Subgroup analysis and interactions A subgroup analysis was conducted to assess the stability of the positive correlation between the CTI index and new-onset stroke. Participants were categorised into distinct subgroups based on their sociodemographic characteristics, lifestyle, dietary habits and medical history. The association was then analysed within each subgroup (see Fig. 3 ). The results indicate that elevated CTI levels were consistently observed across different subgroups, including never smokers and current smokers, as well as those who never drank alcohol, those who drank less than once a month and those who drank more than once a month. The results also showed that elevated CTI levels were observed in people who slept for less than seven hours and those who slept for more than seven hours, as well as in people with a BMI of less than 18 and people with a BMI of 28 or more. This pattern remained consistent regardless of gender, age, educational attainment, marital status, place of residence, hypertension or diabetes. Further interaction analyses showed that the link between the CTI index and stroke risk was consistent regardless of the stratification factor (interaction P-values > 0.05). The predictive capability of the CTI index for strokes. The employment of ROC analysis was for the purpose of comparing the predictive capabilities of the CTI index and TyG for stroke risk. The results showed that the CTI index had an AUC value of 0.607 (95% CI: 0.403–0.726), while the TyG index had an AUC value of 0.553 (95% CI: 0.713–0.373). The CTI index demonstrated superior diagnostic efficacy for stroke incidence compared to TyG, suggesting that it may be a more effective predictor for stroke risk stratification.(see Fig. 4 ) Discussion This study uses CHARLS data to address a gap in the literature concerning the relationship between the CTI index and the risk of a first stroke. The key findings are as follows: Firstly, the CTI index is an independent risk factor for stroke, with an increased risk of stroke as CTI levels rise. Secondly, a non-linear relationship was observed between CTI and stroke risk. Thirdly, the CTI index demonstrated superior predictive value for stroke risk compared to the traditional TyG composite indicator. Taken together, these findings suggest that the CTI index could be considered a reliable predictor of stroke risk, highlighting its importance in the development of stroke prevention and intervention strategies. The CTI index combines CRP, a recognised inflammatory biomarker, and the TyG index, a biomarker of insulin resistance. Previous studies have shown that high TyG index levels are associated with an increased risk of stroke 22 , 23 . Specifically, Huo et al.'s study of 8,231 participants from the CHARLS database found that a higher TyG index at baseline was associated with an increased risk of stroke in middle-aged and elderly Chinese individuals 24 . Furthermore, Cai et al. found that changes in the TyG index were significantly associated with all-cause mortality in critically ill stroke patients. Persistently elevated TyG indices were found to be correlated with hospital mortality 25 . Yang et al. conducted a meta-analysis of 592,635 participants, which revealed a significant correlation between high TyG indices and various negative stroke outcomes 26 . On the other hand, inflammation is another significant risk factor for stroke. Research by Irimie et al. suggests that higher levels of CRP at the time of hospital admission are associated with more severe strokes 27 . Furthermore, a meta-analysis of 11 prospective cohorts involving 10,148 patients revealed a link between elevated C-reactive protein levels and an increased risk of recurrent stroke 28 . In our study, the CTI had a higher odds ratio. ROC analysis revealed a tendency for the CTI to exhibit greater predictive value for stroke than the TyG index. This approach could help clinicians to identify patients at high risk of stroke, enabling them to assess risk accurately and implement targeted interventions to reduce incidence. Compared to using the TyG index alone, employing the CTI provides a more comprehensive understanding of an individual's health status. This is because the TyG index reflects underlying insulin resistance, while CRP offers insight into inflammation. Furthermore, TyG and CRP offer a deeper understanding of insulin resistance and inflammatory changes from different perspectives. Together, they enhance the ability to identify and stratify high-risk individuals. In our study, we analysed data from 7,295 participants from the CHARLS database. Our findings indicate a significant correlation between moderate-to-high CTI levels and stroke prevalence. Furthermore, RCS analysis revealed a clear non-linear relationship between CTI levels and stroke occurrence. In order to reduce the incidence of stroke among middle-aged and elderly individuals, clinicians should aim to lower CTI levels. Our research further indicates that the association between CTI and stroke risk is similar for both genders, with women having a 6% higher risk of stroke than men. Interestingly, Madsen et al.'s study found that women over the age of 20 experienced a higher incidence of stroke than men, and this pattern was also observed in subsequent studies examining transient ischaemic attacks 29 , 30 . Therefore, clinicians should prioritise monitoring CTI levels in women. Furthermore, given that individuals aged 60 and over have higher odds ratios, this demographic should also be given particular attention. We used an RCS curve to explore the dose-response relationship between CTI and stroke. The results revealed a significant overall association between CTI and stroke risk ( P _(overall) = 0.0004). Importantly, we identified a significant non-linear relationship ( P _(non-linear) = 0.0011), indicating that the impact of CTI on stroke risk is not linear. The shape of the curve shows that when the CTI level falls within the range of 10–11, the risk of stroke increases, whereas when the CTI exceeds 11, the rate of increase levels off. This non-linear pattern has significant clinical implications, suggesting the existence of a critical CTI threshold beyond which the rate of increase in risk changes. However, previous studies have reported a linear association between the TyG index and stroke risk 31 . This non-linear relationship may reflect the complex interplay between chronic, sub-optimal inflammation and metabolic disorders. These disorders exhibit a 'tipping point' or 'saturation effect' in the cumulative vascular damage they cause. Although the precise mechanisms linking CTI to stroke risk are not fully understood, the following pathophysiological processes may be involved. Firstly, insulin resistance and the resulting inflammatory response can cause endothelial dysfunction, reduce nitric oxide bioavailability and disrupt haemostasis, thereby accelerating the progression of atherosclerosis. Together, these factors contribute to an increased risk of stroke 32 – 34 . Secondly, the inflammatory state may exacerbate insulin resistance further, prompting tissues to release additional inflammatory mediators and perpetuating a vicious cycle of the systemic inflammatory response. These two factors interact synergistically, collectively increasing the likelihood of a stroke 35 . Finally, inflammation and insulin resistance can both compromise the stability of atherosclerotic plaques, increasing their risk of rupturing. This can subsequently trigger thrombosis and ultimately lead to a stroke 36 . Patients exhibiting insulin resistance and chronic inflammation often have other metabolic abnormalities, such as hypertension, diabetes mellitus and obesity. These conditions are significant risk factors for stroke 37 – 41 . Therefore, it is reasonable to hypothesise that individuals with higher CTI levels may experience more severe vascular damage and a greater risk of stroke. However, the precise molecular mechanisms by which CTI influences stroke remain to be explored further. This study sheds light on the impact of CTI on stroke risk in individuals aged 45 years and over. It provides new evidence to help integrate existing data and develop comprehensive clinical prevention strategies. In light of the ongoing global increase in stroke incidence and the significantly higher risk among populations experiencing inflammation and insulin resistance, the early identification of individuals at high risk is crucial in addressing this challenge. The findings directly inform clinical practice, enabling more effective stroke prevention by monitoring and controlling CTI within target ranges. This study has several notable strengths. Firstly, it is a prospective, nationwide, longitudinal cohort study with a large sample size, which enhances the reliability of the findings. Secondly, subgroup analyses were conducted to assess the consistency of results across different demographic characteristics. Thirdly, we employed ROC curve analysis to compare the predictive capabilities of the CTI and TyG indices for stroke, providing valuable insights for clinical practice. There are certain limitations associated with this study that must be acknowledged. Firstly, participants were asked to self-report their stroke diagnoses based on assessments by healthcare providers, which may not accurately reflect the actual incidence of stroke. Secondly, blood samples were only collected during the first and third waves, so additional follow-up data is needed for a more in-depth analysis. Additionally, the CHARLS questionnaire lacks classification of stroke subtypes, which constitutes another limitation. Thirdly, while model 3 did include multivariate adjustment for confounding factors, the odds ratio for the Q4 group remained at 1.32, indicating an increased risk trend. However, the 95% confidence interval is relatively wide, spanning the 1.0 threshold. This may be due to the small number of participants in the Q4 group, which results in insufficient statistical power to detect a moderate true effect. Therefore, we cannot conclude that the Q4 group is risk-free. Rather, statistical significance has not been attained with the current sample size. Fourthly, the CTI was developed using data from the Chinese population. Due to racial and geographical variations, further multicentre studies are required to adjust and validate the formula for specific populations, to ensure its applicability and accuracy when applied to populations in other countries in future. Conclusion Overall, this study shows that the CTI index is an independent risk factor for new-onset stroke. Our findings suggest that the most cost-effective strategy for stroke prevention is to target interventions at individuals with a rising TyG index from moderate levels. Abbreviations TyG Triglyceride-glucose CTI C-reactive protein-triglyceride-glucose CHARLS China Health and Retirement Longitudinal Study RCS Restricted cubic splines ROC Receiver operating characteristic AUC Area under the curve IR Insulin resistance CRP C-reactive protein BMI Body mass index TG Triglycerides FBG Fasting blood glucose HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol IQR Interquartile range, SD Standard deviation, OR Odds ratios Declarations Ethics statement The CHARLS study protocol received approval from the Peking University Ethics Review Committee (IRB00001052-11015), Beijing, China. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study. Consent to Participate Informed consent was obtained from all individual participants included in the study. Clinical trial number Not applicable. Human Ethics and Consent to Participate declarations Not applicable. Data availability statement The data from this study can be accessed online at https://charls.pku.edu.cn/. To obtain the data, you will need to register as a user on the website. Once your registration has been reviewed and approved, you can follow the provided instructions to download the dataset. Conflict of interest No conflicts of interest are disclosed by any of the writers. Funding None. Author contributions MCW: Research design; methodology; software application; data validation; drafting of the initial manuscript. GW and XXB: Research design; review and editing. HLW and XYW: Data collation, research design and drafting of the initial manuscript. 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Triglyceride-glucose index as a potential predictor for in-hospital mortality in critically ill patients with intracerebral hemorrhage: a multicenter, case-control study. BMC Geriatr. 2024;24(1):385.10.1186/s12877-024-05002-4. Li S, Yin C, Zhao W, Zhu H, Xu D, Xu Q, Jiao Y, Wang X, Qiao H. Homeostasis model assessment of insulin resistance in relation to the poor functional outcomes in nondiabetic patients with ischemic stroke. Biosci Rep. 2018;38(3).10.1042/bsr20180330. Jin A, Wang S, Li J, Wang M, Lin J, Li H, Meng X, Wang Y, Pan Y. Mediation of Systemic Inflammation on Insulin Resistance and Prognosis of Nondiabetic Patients With Ischemic Stroke. Stroke. 2023;54(3):759-769.10.1161/strokeaha.122.039542. Ma Y, Zhen Y, Wang M, Gao L, Dang Y, Shang J, Chen X, Ma S, Zhou K, Feng K, Xin Y, Hou Y, Guo C. Associations between the serum triglyceride-glucose index and pericoronary adipose tissue attenuation and plaque features using dual-layer spectral detector computed tomography: a cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1166117.10.3389/fendo.2023.1166117. Püschel GP, Klauder J, Henkel J, Macrophages L-G, Inflammation. Insulin Resistance and Hyperinsulinemia: A Mutual Ambiguous Relationship in the Development of Metabolic Diseases. J Clin Med. 2022;11(15).10.3390/jcm11154358. Du Y, Wei F, Dong Z, Liu J, Jiang W, Lu Q. Prognostic value of serum LP-PLA2 and hs-CRP in unstable atherosclerotic plaques. Clin Exp Hypertens. 2011;33(2):113-116.10.3109/10641963.2010.531836. Zimmet P, Boyko EJ, Collier GR, de Courten M. Etiology of the metabolic syndrome: potential role of insulin resistance, leptin resistance, and other players. Ann N Y Acad Sci. 1999;892:25-44.10.1111/j.1749-6632.1999.tb07783.x. Meex RCR, Blaak EE, van Loon LJC. Lipotoxicity plays a key role in the development of both insulin resistance and muscle atrophy in patients with type 2 diabetes. Obes Rev. 2019;20(9):1205-1217.10.1111/obr.12862. Lee YH, Pratley RE. The evolving role of inflammation in obesity and the metabolic syndrome. Curr Diab Rep. 2005;5(1):70-75.10.1007/s11892-005-0071-7. Richardson VR, Smith KA, Carter AM. Adipose tissue inflammation: feeding the development of type 2 diabetes mellitus. Immunobiology. 2013;218(12):1497-1504.10.1016/j.imbio.2013.05.002. McMaster WG, Kirabo A, Madhur MS, Harrison DG. Inflammation, immunity, and hypertensive end-organ damage. Circ Res. 2015;116(6):1022-1033.10.1161/circresaha.116.303697. Additional Declarations No competing interests reported. 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16:16:17","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159393,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8100524/v1/981c3eba98744da386ce8f46.html"},{"id":97265189,"identity":"a5a4c320-cf7a-444c-82a1-24f6a7e8d7af","added_by":"auto","created_at":"2025-12-02 14:26:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":199735,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study population\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8100524/v1/d566d331bba4c00a788f9fa8.png"},{"id":97368461,"identity":"c726de91-5da4-42fd-9228-51b64f694f02","added_by":"auto","created_at":"2025-12-03 16:22:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30835,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of the CTI index and the risk of stroke\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8100524/v1/7466328db53ef3f32332a45a.png"},{"id":97265193,"identity":"3272f2fe-69a8-49cd-8f60-6c6fec2f86c9","added_by":"auto","created_at":"2025-12-02 14:26:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99662,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of the CTI index with stroke risk stratified by different factors\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8100524/v1/184c76dab382aa382124e35b.png"},{"id":97368143,"identity":"7cc48cea-6ebf-4981-9275-e7e4d787ea79","added_by":"auto","created_at":"2025-12-03 16:21:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56667,"visible":true,"origin":"","legend":"\u003cp\u003eA comparison of the predictive ability of the CTI and TyG indices for stroke risk\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8100524/v1/2bd7b297e96114df7b721e2a.png"},{"id":97664713,"identity":"ade2c10a-0441-47fa-a72f-af8427d0eedb","added_by":"auto","created_at":"2025-12-08 09:13:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1289767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8100524/v1/07cc57e3-85be-4fe1-9613-92a323bd60d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between C-reactive protein-triglyceride glucose index and the risk of new-onset stroke in middle-aged and elderly individuals: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is the primary cause of death and disability, and its global incidence is increasing, which heightens the risk of adverse outcomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In China, stroke accounts for the highest disability-adjusted life-years lost of all diseases. Driven by population ageing and the widespread prevalence of risk factors such as hypertension and inadequate control measures, the future burden of this disease is projected to continuously escalate, imposing significant economic and social pressures on families and wider society\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite advances in prevention and treatment, the incidence of stroke continues to rise\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, prior research indicates that insulin resistance (IR) is a factor that increases the risk of stroke\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Consequently, identifying stroke risk factors and implementing effective interventions are crucial for prevention and delaying disease progression.\u003c/p\u003e\u003cp\u003eIR denotes diminished physiological insulin action within the body and may contribute to the development of atherosclerosis and the onset and progression of stroke\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Due to its simplicity and practicality, the TyG index is widely used by researchers as a marker for IR\u003csup\u003e8\u003c/sup\u003e. There is substantial evidence supporting its efficacy in predicting conditions such as hypertension, atrial fibrillation, diabetes mellitus and stroke\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, inflammation is recognised as a critically important risk factor for stroke\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. It significantly increases stroke incidence by promoting atherosclerosis, impairing vascular endothelial function and increasing thrombogenicity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. C-reactive protein (CRP), a non-specific inflammatory marker, is closely associated with stroke risk and has emerged as a promising biomarker for stroke risk assessment\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Insulin resistance and vascular inflammation are key drivers in the pathogenesis of stroke\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, developing composite indicators reflecting insulin resistance and inflammation as tools for predicting stroke is of considerable importance. Ruan et al. first proposed the CTI\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which comprehensively reflects inflammation and insulin resistance, demonstrating strong predictive value for prognosis in diabetic patients, cancer patients, and cardiovascular disease incidence\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, the association between CTI and stroke risk remains unclear.\u003c/p\u003e\u003cp\u003eIn order to address these critical research gaps, we analysed data from the China Health and Retirement Longitudinal Study (CHARLS) in order to explore the complex relationship between CTI and stroke risk. This provides further evidence for the practical application of CTI in real-world settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eOur research data analysis is based on the CHARLS, which is a nationwide study designed to examine issues related to ageing. The first survey was conducted in 2011\u0026ndash;12 (Wave 1), with subsequent follow-up surveys taking place every two to three years. The survey sample covers 150 county-level and 450 village-level units, comprising participants aged 45 years and over. All participants provided written informed consent. The analysis included 17,705 individuals who completed blood tests at baseline (Wave 1). After excluding those who met the following criteria, the final cohort comprised 7,295 individuals: age\u0026thinsp;\u0026lt;\u0026thinsp;45 years; missing baseline data for age, sex, place of origin, marital status, triglycerides (TG), fasting blood glucose (FBG) or body mass index(BMI)\u0026thinsp;\u0026le;\u0026thinsp;100; a history of stroke prior to 2011; the use of hypoglycaemic or lipid-lowering medication; or missing baseline covariate data. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the detailed screening process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment of the CTI index\u003c/h3\u003e\n\u003cp\u003eData on age, gender, TG, FBG and CRP were collected from the first round of surveys. The following index was calculated using an established method\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e: CTI\u0026thinsp;=\u0026thinsp;0.412 \u0026times; Ln (CRP [mg/L])\u0026thinsp;+\u0026thinsp;Ln (TG [mg/dl] \u0026times; FPG [mg/dl])/2.\u003c/p\u003e\n\u003ch3\u003eAssessment of new-onset stroke\u003c/h3\u003e\n\u003cp\u003eThe diagnosis of new-onset stroke was based on self-reported data. Stroke events that occurred during the fourth follow-up wave (in 2018) were considered outcome measures. Respondents who answered 'yes' to the interviewer's question 'Have you ever been diagnosed with a stroke by a doctor?' were classified as stroke patients. Participants who had experienced a stroke in 2011 were excluded. Patients who were subsequently diagnosed with a stroke during the follow-up period up to 2018 were included in the study according to our definition of new-onset stroke.\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eThe following covariates have been selected: (1) Socio-demographic characteristics: age; gender (male/female); marital status (married/unmarried); place of residence (urban/rural); educational attainment (university and above/secondary school/primary school); (2) Lifestyle and dietary habits: Smoking (\u0026ldquo;Yes\u0026rdquo; or \u0026ldquo;No\u0026rdquo;); alcohol consumption (\u0026ldquo;Drinks less than once a month\u0026rdquo;, \u0026ldquo;Drinks more than once a month\u0026rdquo; or \u0026ldquo;Does not drink\u0026rdquo;); average nightly sleep duration over the past month; (3) Medical history: Hypertension (present/absent); diabetes (present/absent); (4) Laboratory tests, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and blood glucose; (5) Physical examination, including BMI.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe statistical analysis was based on data derived from CHARLS surveys conducted between 2011 and 2018. Our study encompassed 7,295 participants. For continuous variables, the data are presented as the mean (standard deviation, SD) or the median (interquartile range, IQR), and for categorical variables, the data are expressed as percentages. We used multivariate logistic regression models to examine the relationship between the CTI index and new-onset stroke, adjusting for relevant variables within the models. Model 1 was unadjusted, Model 2 included adjustments for gender, age, place of residence, marital status and educational attainment, and Model 3 incorporated further adjustments for smoking and drinking status, hypertension, sleep duration, diabetes, blood glucose levels, BMI, HDL and LDL.To explore potential non-linear relationships between the CTI index and stroke risk, we subsequently applied RCS analysis. Subgroup analyses were then employed to evaluate multiple regression stratified by subgroup, examining interactions to determine whether different covariates, such as sociodemographic, lifestyle and health-related factors, influenced the association between the CTI index and stroke risk. Finally, we employed ROC curve analysis to compare the predictive capabilities of the CTI and TyG indices for stroke. Statistical analysis was conducted using R version 4.4.3, with two-tailed P-values below 0.05 being considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e\u003cp\u003eThe participant screening process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the study included 7,295 participants, with an average age of 58.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75 years. Of these participants, 4,006 (55%) were female. By the end of the follow-up period, 456 participants had experienced a stroke. The highest incidence rates were observed in quartiles 1, 2, 3 and 4, at 3.7%, 5.9%, 8.0% and 7.4% respectively. Furthermore, individuals in the highest quartile of CTI were more likely to be female, reside in rural areas, have shorter sleeping times, smoke less frequently, and abstain from alcohol consumption than those in the lowest quartile. They also exhibited a higher prevalence of hypertension, diabetes mellitus, and dyslipidaemia. Regarding physical examinations and laboratory assessments, higher CTI quartiles were associated with higher BMI and blood glucose levels.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;7295\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1824\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1824\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1823\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1824\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.95\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;006 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e858 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e985 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u0026thinsp;077 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026thinsp;086 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026thinsp;289 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e966 (53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e839 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e746 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e738 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh shcool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e638 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e158 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026thinsp;565 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;640 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026thinsp;639 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u0026thinsp;642 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026thinsp;644 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026thinsp;229 (85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;534 (84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026thinsp;552 (85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u0026thinsp;544 (85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026thinsp;599 (88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;066 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e290 (16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e272 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e279 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e225 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e485 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e160 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVillage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026thinsp;810 (93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;725 (95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026thinsp;709 (94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u0026thinsp;712 (94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026thinsp;664 (91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;759 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e781 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e707 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e642 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e629 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink but less than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e572 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink more than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;821 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e527 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e459 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e410 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e425 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;902 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026thinsp;141 (63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026thinsp;219 (67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u0026thinsp;283 (70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026thinsp;259 (69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleeping Time, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.37\u0026plusmn; (1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.37\u0026plusmn; (1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.31\u0026plusmn; (1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.34\u0026plusmn; (1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.45\u0026plusmn; (1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;665 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e276 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e339 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e469 (26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e581 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e350 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(Kg/m\u0026sup2;, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.60\u0026plusmn; (3.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.21\u0026plusmn; (3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.10\u0026plusmn; (4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.93\u0026plusmn; (4.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.16\u0026plusmn; (3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mg/dl, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109.14\u0026plusmn; (33.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.34\u0026plusmn; (14.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.15\u0026plusmn; (13.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e106.68\u0026plusmn; (18.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e133.37\u0026plusmn; (54.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mg/dl, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.40\u0026plusmn; (15.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.67\u0026plusmn; (15.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.82\u0026plusmn; (14.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.16\u0026plusmn; (12.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.96\u0026plusmn; (11.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mg/dl, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116.67\u0026plusmn; (34.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.11\u0026plusmn; (28.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.45\u0026plusmn; (30.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e123.64\u0026plusmn; (34.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e114.48\u0026plusmn; (41.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u0026middot;73\u0026plusmn; (0\u0026middot;84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u0026middot;75\u0026plusmn; (0\u0026middot;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u0026middot;39\u0026plusmn; (0\u0026middot;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u0026middot;91\u0026plusmn; (0\u0026middot;16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u0026middot;85\u0026plusmn; (0\u0026middot;57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG (mg/dl, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.68\u0026plusmn; (0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.93\u0026plusmn; (0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.41\u0026plusmn; (0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.80\u0026plusmn; (0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.56\u0026plusmn; (0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e456 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e146 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote:HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol;\u0026nbsp;Sleeping Time:Average Hours for One Night Sleeping Time During the Past Month;\u0026nbsp;BMI: body mass index; TyG: triglyceride-glucose index;\u0026nbsp;\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCorrelation between CTI Index and Risk of New Stroke\u003c/h3\u003e\n\u003cp\u003eTo investigate the association between CTI indices and stroke risk, this study employed logistic regression analysis so that the relationship between CTI quartiles and stroke risk could be assessed, and this was quantified through the calculation of odds ratios (OR) and 95% confidence intervals (95% CI). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the multivariate regression analysis. The results indicate that, in the fully adjusted model (Model 3), each additional unit of CTI in the Q2 group was associated with a 48% increased risk of stroke compared with the Q1 group (OR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.08\u0026ndash;2.05). For the Q3 group, each additional unit of CTI was associated with a 75% increased risk of stroke compared with the Q1 group (OR\u0026thinsp;=\u0026thinsp;1.75, 95% CI: 1.27\u0026ndash;2.43). This suggests that stroke risk increases with higher CTI levels. However, this association was not statistically significant for the Q4 group (the highest quartile) (OR\u0026thinsp;=\u0026thinsp;1.32, 95% CI: 0.92\u0026ndash;1.92).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProspective associations between baseline CTI with follow-up incident stroke\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.65(1.21, 2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.68(1.23, 2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.48(1.08, 2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.28(1.70, 3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.31(1.72, 3.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.75(1.27, 2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.10(1.56, 2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.21(1.64, 3.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32(0.92, 1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Model 1 was crude model. Model 2 was adjusted for age, gender, education level, location and marital status. Model 3 was further for smoking, drinking, sleep, hypertension, diabetes, ldl Cholesterol, hdl Cholesterol, BMI and glucose.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDose-response relationship between CTI index and stroke risk\u003c/h2\u003e\u003cp\u003eRCS analysis was employed to evaluate the potential non-linear association between the CTI index and stroke risk. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates these findings, revealing a U-shaped relationship between the CTI index and stroke within the fully adjusted model (\u003cem\u003eP\u003c/em\u003e_overall\u0026thinsp;=\u0026thinsp;0.0004, \u003cem\u003eP\u003c/em\u003e_non-linear\u0026thinsp;=\u0026thinsp;0.0011).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis and interactions\u003c/h2\u003e\u003cp\u003eA subgroup analysis was conducted to assess the stability of the positive correlation between the CTI index and new-onset stroke. Participants were categorised into distinct subgroups based on their sociodemographic characteristics, lifestyle, dietary habits and medical history. The association was then analysed within each subgroup (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results indicate that elevated CTI levels were consistently observed across different subgroups, including never smokers and current smokers, as well as those who never drank alcohol, those who drank less than once a month and those who drank more than once a month. The results also showed that elevated CTI levels were observed in people who slept for less than seven hours and those who slept for more than seven hours, as well as in people with a BMI of less than 18 and people with a BMI of 28 or more. This pattern remained consistent regardless of gender, age, educational attainment, marital status, place of residence, hypertension or diabetes. Further interaction analyses showed that the link between the CTI index and stroke risk was consistent regardless of the stratification factor (interaction P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe predictive capability of the CTI index for strokes.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe employment of ROC analysis was for the purpose of comparing the predictive capabilities of the CTI index and TyG for stroke risk. The results showed that the CTI index had an AUC value of 0.607 (95% CI: 0.403\u0026ndash;0.726), while the TyG index had an AUC value of 0.553 (95% CI: 0.713\u0026ndash;0.373). The CTI index demonstrated superior diagnostic efficacy for stroke incidence compared to TyG, suggesting that it may be a more effective predictor for stroke risk stratification.(see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study uses CHARLS data to address a gap in the literature concerning the relationship between the CTI index and the risk of a first stroke. The key findings are as follows: Firstly, the CTI index is an independent risk factor for stroke, with an increased risk of stroke as CTI levels rise. Secondly, a non-linear relationship was observed between CTI and stroke risk. Thirdly, the CTI index demonstrated superior predictive value for stroke risk compared to the traditional TyG composite indicator. Taken together, these findings suggest that the CTI index could be considered a reliable predictor of stroke risk, highlighting its importance in the development of stroke prevention and intervention strategies.\u003c/p\u003e\u003cp\u003eThe CTI index combines CRP, a recognised inflammatory biomarker, and the TyG index, a biomarker of insulin resistance. Previous studies have shown that high TyG index levels are associated with an increased risk of stroke\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Specifically, Huo et al.'s study of 8,231 participants from the CHARLS database found that a higher TyG index at baseline was associated with an increased risk of stroke in middle-aged and elderly Chinese individuals\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Furthermore, Cai et al. found that changes in the TyG index were significantly associated with all-cause mortality in critically ill stroke patients. Persistently elevated TyG indices were found to be correlated with hospital mortality\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Yang et al. conducted a meta-analysis of 592,635 participants, which revealed a significant correlation between high TyG indices and various negative stroke outcomes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. On the other hand, inflammation is another significant risk factor for stroke. Research by Irimie et al. suggests that higher levels of CRP at the time of hospital admission are associated with more severe strokes\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Furthermore, a meta-analysis of 11 prospective cohorts involving 10,148 patients revealed a link between elevated C-reactive protein levels and an increased risk of recurrent stroke\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In our study, the CTI had a higher odds ratio. ROC analysis revealed a tendency for the CTI to exhibit greater predictive value for stroke than the TyG index. This approach could help clinicians to identify patients at high risk of stroke, enabling them to assess risk accurately and implement targeted interventions to reduce incidence. Compared to using the TyG index alone, employing the CTI provides a more comprehensive understanding of an individual's health status. This is because the TyG index reflects underlying insulin resistance, while CRP offers insight into inflammation. Furthermore, TyG and CRP offer a deeper understanding of insulin resistance and inflammatory changes from different perspectives. Together, they enhance the ability to identify and stratify high-risk individuals.\u003c/p\u003e\u003cp\u003eIn our study, we analysed data from 7,295 participants from the CHARLS database. Our findings indicate a significant correlation between moderate-to-high CTI levels and stroke prevalence. Furthermore, RCS analysis revealed a clear non-linear relationship between CTI levels and stroke occurrence. In order to reduce the incidence of stroke among middle-aged and elderly individuals, clinicians should aim to lower CTI levels. Our research further indicates that the association between CTI and stroke risk is similar for both genders, with women having a 6% higher risk of stroke than men. Interestingly, Madsen et al.'s study found that women over the age of 20 experienced a higher incidence of stroke than men, and this pattern was also observed in subsequent studies examining transient ischaemic attacks\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Therefore, clinicians should prioritise monitoring CTI levels in women. Furthermore, given that individuals aged 60 and over have higher odds ratios, this demographic should also be given particular attention. We used an RCS curve to explore the dose-response relationship between CTI and stroke. The results revealed a significant overall association between CTI and stroke risk (\u003cem\u003eP\u003c/em\u003e_(overall)\u0026thinsp;=\u0026thinsp;0.0004). Importantly, we identified a significant non-linear relationship (\u003cem\u003eP\u003c/em\u003e_(non-linear)\u0026thinsp;=\u0026thinsp;0.0011), indicating that the impact of CTI on stroke risk is not linear. The shape of the curve shows that when the CTI level falls within the range of 10\u0026ndash;11, the risk of stroke increases, whereas when the CTI exceeds 11, the rate of increase levels off. This non-linear pattern has significant clinical implications, suggesting the existence of a critical CTI threshold beyond which the rate of increase in risk changes. However, previous studies have reported a linear association between the TyG index and stroke risk\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This non-linear relationship may reflect the complex interplay between chronic, sub-optimal inflammation and metabolic disorders. These disorders exhibit a 'tipping point' or 'saturation effect' in the cumulative vascular damage they cause.\u003c/p\u003e\u003cp\u003eAlthough the precise mechanisms linking CTI to stroke risk are not fully understood, the following pathophysiological processes may be involved. Firstly, insulin resistance and the resulting inflammatory response can cause endothelial dysfunction, reduce nitric oxide bioavailability and disrupt haemostasis, thereby accelerating the progression of atherosclerosis. Together, these factors contribute to an increased risk of stroke\u003csup\u003e\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Secondly, the inflammatory state may exacerbate insulin resistance further, prompting tissues to release additional inflammatory mediators and perpetuating a vicious cycle of the systemic inflammatory response. These two factors interact synergistically, collectively increasing the likelihood of a stroke\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Finally, inflammation and insulin resistance can both compromise the stability of atherosclerotic plaques, increasing their risk of rupturing. This can subsequently trigger thrombosis and ultimately lead to a stroke\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Patients exhibiting insulin resistance and chronic inflammation often have other metabolic abnormalities, such as hypertension, diabetes mellitus and obesity. These conditions are significant risk factors for stroke\u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Therefore, it is reasonable to hypothesise that individuals with higher CTI levels may experience more severe vascular damage and a greater risk of stroke. However, the precise molecular mechanisms by which CTI influences stroke remain to be explored further.\u003c/p\u003e\u003cp\u003eThis study sheds light on the impact of CTI on stroke risk in individuals aged 45 years and over. It provides new evidence to help integrate existing data and develop comprehensive clinical prevention strategies. In light of the ongoing global increase in stroke incidence and the significantly higher risk among populations experiencing inflammation and insulin resistance, the early identification of individuals at high risk is crucial in addressing this challenge. The findings directly inform clinical practice, enabling more effective stroke prevention by monitoring and controlling CTI within target ranges.\u003c/p\u003e\u003cp\u003eThis study has several notable strengths. Firstly, it is a prospective, nationwide, longitudinal cohort study with a large sample size, which enhances the reliability of the findings. Secondly, subgroup analyses were conducted to assess the consistency of results across different demographic characteristics. Thirdly, we employed ROC curve analysis to compare the predictive capabilities of the CTI and TyG indices for stroke, providing valuable insights for clinical practice.\u003c/p\u003e\u003cp\u003eThere are certain limitations associated with this study that must be acknowledged. Firstly, participants were asked to self-report their stroke diagnoses based on assessments by healthcare providers, which may not accurately reflect the actual incidence of stroke. Secondly, blood samples were only collected during the first and third waves, so additional follow-up data is needed for a more in-depth analysis. Additionally, the CHARLS questionnaire lacks classification of stroke subtypes, which constitutes another limitation. Thirdly, while model 3 did include multivariate adjustment for confounding factors, the odds ratio for the Q4 group remained at 1.32, indicating an increased risk trend. However, the 95% confidence interval is relatively wide, spanning the 1.0 threshold. This may be due to the small number of participants in the Q4 group, which results in insufficient statistical power to detect a moderate true effect. Therefore, we cannot conclude that the Q4 group is risk-free. Rather, statistical significance has not been attained with the current sample size. Fourthly, the CTI was developed using data from the Chinese population. Due to racial and geographical variations, further multicentre studies are required to adjust and validate the formula for specific populations, to ensure its applicability and accuracy when applied to populations in other countries in future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, this study shows that the CTI index is an independent risk factor for new-onset stroke. Our findings suggest that the most cost-effective strategy for stroke prevention is to target interventions at individuals with a rising TyG index from moderate levels.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTyG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Triglyceride-glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCTI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; C-reactive protein-triglyceride-glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHARLS \u0026nbsp; \u0026nbsp; \u0026nbsp;China Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eRCS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Restricted cubic splines\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Insulin resistance \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Triglycerides\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFBG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fasting blood glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-density lipoprotein cholesterol \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Low-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Interquartile range,\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Standard deviation,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Odds ratios\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS study protocol received approval from the Peking University Ethics Review Committee (IRB00001052-11015), Beijing, China. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from this study can be accessed online at https://charls.pku.edu.cn/. To obtain the data, you will need to register as a user on the website. Once your registration has been reviewed and approved, you can follow the provided instructions to download the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflicts of interest are disclosed by any of the writers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMCW: Research design; methodology; software application; data validation; drafting of the initial manuscript. GW and XXB: Research design; review and editing. HLW and XYW: Data collation, research design and drafting of the initial manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from the CHARLS database. The authors would like to express their gratitude to the CHARLS research team and to all the individuals who participated in the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMa Q, Li R, Wang L, Yin P, Wang Y, Yan C, Ren Y, Qian Z, Vaughn MG, McMillin SE, Hay SI, Naghavi M, Cai M, Wang C, Zhang Z, Zhou M, Lin H, Yang Y. Temporal trend and attributable risk factors of stroke burden in China, 1990\u0026ndash;2019: an analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Public Health.\u003c/em\u003e 2021;6(12):e897-e906.10.1016/s2468-2667(21)00228-0.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu S, Wu B, Liu M, Chen Z, Wang W, Anderson CS, Sandercock P, Wang Y, Huang Y, Cui L, Pu C, Jia J, Zhang T, Liu X, Zhang S, Xie P, Fan D, Ji X, Wong KL, Wang L. 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Inflammation, immunity, and hypertensive end-organ damage. \u003cem\u003eCirc Res.\u003c/em\u003e 2015;116(6):1022-1033.10.1161/circresaha.116.303697.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"C-reactive protein-triglyceride glucose index, stroke, China Health and Retirement Longitudinal Study, inflammation, retrospective study","lastPublishedDoi":"10.21203/rs.3.rs-8100524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8100524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e The impact of the triglyceride-glucose (TyG) index on stroke incidence has been investigated in numerous studies. However, the relationship between the C-reactive protein-triglyceride-glucose (CTI) index, which is a new marker of insulin resistance and inflammation, and stroke is unclear.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e We analysed data from 7,295 participants in the China Health and Retirement Longitudinal Study (CHARLS). The association of the CTI index with incident stroke was assessed using something called 'logistic regression'. Potential nonlinearity was explored using restricted cubic splines (RCS), and the stability of the results was evaluated through subgroup analyses. Furthermore, we compared the predictive performance of the CTI and TyG indices by calculating their respective areas under the receiver operating characteristic (ROC) curve.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e During the follow-up period, 456 participants experienced a stroke. After adjusting for confounding factors (Model 3), higher and progressively increasing CTI indices in the Q2 (OR\u0026thinsp;=\u0026thinsp;1.48, 95% CI 1.08\u0026ndash;2.05) and Q3 (OR\u0026thinsp;=\u0026thinsp;1.75, 95% CI 1.27\u0026ndash;2.43) groups were associated with an increased risk of new-onset stroke. Although the OR for Q4 was elevated, it was not statistically significant (OR\u0026thinsp;=\u0026thinsp;1.32, 95% CI 0.92\u0026ndash;1.92). RCS analysis revealed a U-shaped relationship between CTI index and stroke risk. ROC analysis revealed that the CTI index had an area under the curve (AUC) of 0.607 (95% confidence interval: 0.403\u0026ndash;0.726), whereas the TyG index had an AUC of 0.553 (95% confidence interval: 0.713\u0026ndash;0.373). This finding suggests that the CTI index is a more reliable indicator of stroke risk than the TyG index.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e Overall, this study demonstrates that the CTI index is an independent risk factor for new-onset stroke. Our findings suggest that the most cost-effective approach to stroke prevention is to implement interventions targeting individuals whose CTI index begins to rise from moderate levels.\u003c/p\u003e","manuscriptTitle":"The association between C-reactive protein-triglyceride glucose index and the risk of new-onset stroke in middle-aged and elderly individuals: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:26:08","doi":"10.21203/rs.3.rs-8100524/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-29T07:07:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T01:53:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313019215260580380972710108417545308755","date":"2025-12-03T09:26:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T13:08:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5031848813999537244380835536611442859","date":"2025-11-29T15:53:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237670020311233881022021795088049344269","date":"2025-11-28T09:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-27T14:19:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-25T17:11:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-15T13:14:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-15T13:13:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-11-13T02:02:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ff0961c-94ee-494f-ad4a-d0c8d42daf97","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T02:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 14:26:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8100524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8100524","identity":"rs-8100524","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}